A phase 3, multicenter, randomized, double-blind study to evaluate the interchangeability of V114, a 15-valent pneumococcal conjugate vaccine, and PCV13 with respect to safety, tolerability, and immunogenicity in healthy infants (PNEU-DIRECTION).

BACKGROUND: Pneumococcal disease (PD) remains a major health concern globally. In children, pneumococcal conjugate vaccines (PCVs) provide protection against PD from most vaccine serotypes, but non-vaccine serotypes contribute to residual disease. V114 is a 15-valent PCV containing all 13 serotypes in Prevnar 13™ (PCV13) and public health important serotypes 22F and 33F. This phase 3 study evaluated safety and immunogenicity of mixed PCV13/V114 regimens using a 3 + 1 dosing schedule when changing from PCV13 to V114 at doses 2, 3, or 4. METHODS: 900 healthy infants were randomized equally to 5 intervention groups. PCVs were administered in a 3-dose infant series at 2, 4, and 6 months of age followed by a toddler dose at 12-15 months along with concomitant routine vaccines. Safety was evaluated as the proportion of participants with adverse events (AEs). Immunoglobulin G (IgG) responses to the 15 serotypes in V114 were measured at 30 days post-dose 3 and 30 days post-dose 4 (PD4). RESULTS: Frequencies of injection-site and systemic AEs were generally comparable across all intervention groups. At 30 days PD4 (primary endpoint), IgG geometric mean concentrations (GMCs) for the 13 shared serotypes were generally comparable between mixed V114/PCV13 and 4-dose regimens of PCV13 or V114. In mixed regimens at 30 days PD4, a toddler dose of V114 was sufficient to achieve IgG GMCs comparable to a 4-dose regimen of V114 for serotype 22F, while at least one infant dose was needed in addition to the toddler dose to achieve IgG GMCs comparable to a 4-dose regimen of V114 for serotype 33F. CONCLUSIONS: V114 was well tolerated with a generally comparable safety profile to PCV13. For 13 shared serotypes, both mixed regimens and the V114 4-dose regimen induced generally comparable antibody responses to 4-dose regimen with PCV13. Study results support interchangeability of V114 with PCV13 in infants. TRIAL REGISTRATION: ClinicalTrials.gov: NCT03620162; EudraCT: 2018-001151-12.

Hemodialysis taping styles and their effect on reducing the chance of venous needle dislodgement.

INTRODUCTION: Vascular access complications are common among hemodialysis patients, although most are not immediately life-threatening. However, inconsistent taping techniques and incompatible detection mechanisms may lead to venous needle dislodgement (VND), which can lead to catastrophic blood loss. Taping technique does not always meet the recommended best practice and there may be no available protocol for new staff. METHODS: Three commonly used taping patterns (the Chevron, butterfly, and overlapping styles) were tested in a mechanical engineering laboratory to determine the forces that each method was capable of withstanding. RESULTS: While all taping styles were confirmed to have an adhesive force stronger than the inherent force from the venous jet flow of blood, the overlapping style was found to have limited capability beyond this minimum criterion. Both the butterfly and Chevron styles demonstrated excellent holding capability, with elongation of dislodgement particularly noted for the butterfly style, and slightly stronger hold noted for the Chevron style. The Chevron style may be better suited to lateral movements from all directions, due to the taping direction. CONCLUSIONS: We recommend that either the Chevron or butterfly style is used for dialysis needle taping, with the butterfly better suited to home dialysis (where monitors may be used) and the Chevron better suited for in-care patients who may present erratic movements. The overlapping style is not recommended for use.

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG.

Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.

Moyamoya syndrome in childhood sickle cell disease: a predictive factor for recurrent cerebrovascular events.

We conducted a retrospective study to determine whether the presence of moyamoya collaterals influenced the risk of recurrence of cerebrovascular events (CVEs: stroke or transient ischemic attack) in patients with sickle cell disease placed on chronic transfusions after a stroke. Forty-three patients with homozygous sickle cell anemia (HbSS) and 1 with HbSO(Arab) (16 females, 28 males) who had suffered strokes while under the age of 18 were studied. All patients had been on transfusions aimed at maintaining the sickle hemoglobin (HbS) level below 30%. They were followed for a mean of 6.6 years (2.2 to 20.4 years). The presence of collaterals was diagnosed based on either magnetic resonance angiography or conventional angiography. Eighteen (41%) of the 44 patients suffered recurrent CVEs. Nineteen (43%) (6 females, 13 males) patients had moyamoya collaterals. Eleven (58%) of these 19 experienced 21 total recurrent CVEs, including 4 strokes in 4 patients (21%). In comparison, 7 (28%) of 25 patients without moyamoya collaterals experienced 9 recurrent CVEs (P <.05) with only 1 recurrent stroke (4%). Moyamoya patients were also more likely to have 2 recurrent CVEs (42% vs 8%, P <.05) as well as poorer neuropsychological testing results. A proportional hazards regression analysis indicated that patients with moyamoya were more than twice as likely to incur a subsequent CVE (hazard ratio, 2.40; 95% confidence interval, 0.85, 6.75). We conclude that up to 41% of patients with sickle cell disease experience recurrent CVEs after an initial stroke despite chronic transfusions and that the risk of recurrence is significantly higher for those who have moyamoya collaterals.